I've been writing some simple machine learning algorithms and looking at time-series data. In doing so, I have come across the use of feature scaling: rescaling and standardizing as they are referred to on Wikipedia.
For something like k-Nearest Neighbors, I imagine rescaling to be the best scaling to use so there's a completely level playing field between variables' influence. Is that true?
When would you apply standardizing to data instead? What are the advantage of standardizing and rescaling?